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Remote Sens. 2018, 10(2), 196; https://doi.org/10.3390/rs10020196

Learning a Dilated Residual Network for SAR Image Despeckling

1
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
2
International School of Software, Wuhan University, Wuhan 430079, China
3
School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China
4
School of Resources and Environmental Engineering, Anhui University, Hefei 230000, China
*
Author to whom correspondence should be addressed.
Received: 13 November 2017 / Revised: 17 January 2018 / Accepted: 24 January 2018 / Published: 29 January 2018
(This article belongs to the Collection Learning to Understand Remote Sensing Images)
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Abstract

In this paper, to break the limit of the traditional linear models for synthetic aperture radar (SAR) image despeckling, we propose a novel deep learning approach by learning a non-linear end-to-end mapping between the noisy and clean SAR images with a dilated residual network (SAR-DRN). SAR-DRN is based on dilated convolutions, which can both enlarge the receptive field and maintain the filter size and layer depth with a lightweight structure. In addition, skip connections and a residual learning strategy are added to the despeckling model to maintain the image details and reduce the vanishing gradient problem. Compared with the traditional despeckling methods, the proposed method shows a superior performance over the state-of-the-art methods in both quantitative and visual assessments, especially for strong speckle noise. View Full-Text
Keywords: SAR image; despeckling; dilated convolution; skip connection; residual learning SAR image; despeckling; dilated convolution; skip connection; residual learning
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Zhang, Q.; Yuan, Q.; Li, J.; Yang, Z.; Ma, X. Learning a Dilated Residual Network for SAR Image Despeckling. Remote Sens. 2018, 10, 196.

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